Work

Support Measure Data Description for group anomaly detection

Year: 2015

Type: conference-paper

Abstract: We address the problem of learning a data description model from a dataset containing probability measures as observations. We estimate the data description model by optimizing volume-sets of probability measures where each volume-set is defined as a set of probability measures whose representative functions in a reproducing kernel Hilbert space (RKHS) belong to an enclosing ball. We present three data description models, which are functions in a RKHS depending only on some probability measures, named support measures in analogy to support vectors. An advantage of the method is that we do not consider any particular form for the probability measures. We validate our method in the task of group anomaly detection, with artificial and real datasets.

Source: HAL

Authors: Guevara, Jorge, Canu, Stéphane, Hirata, R

Institutions: Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes, Universidade de São Paulo

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2418313868

FWCI: 0.872

Cites: 33

Cited by: 14

Related to: 10